


'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__lowercase      :		Optional[int]			=      logging.get_logger(__name__)

__lowercase      :		List[str]			=      {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
__lowercase      :		Any			=      [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Dict  ):
					__a     :			Optional[Any]          =    {}
					with open(_SCREAMING_SNAKE_CASE		, 'r'  ) as file:
										for line_number, line in enumerate(_SCREAMING_SNAKE_CASE  ):
															__a     :			Dict          =    line.strip()
															if line:
																				__a     :			Union[str, Any]          =    line.split()
																				__a     :			Any          =    line_number
																				__a     :			Any          =    words[0]
																				__a     :			Optional[Any]          =    value
					return result






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Union[str, Any]		, _SCREAMING_SNAKE_CASE :  Tuple		, _SCREAMING_SNAKE_CASE :  List[Any]		, _SCREAMING_SNAKE_CASE :  str		, _SCREAMING_SNAKE_CASE :  int  ):
					for attribute in key.split('.'  ):
										__a     :			Optional[Any]          =    getattr(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE  )

					__a     :			Any          =    None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(_SCREAMING_SNAKE_CASE  ):
															__a     :			List[str]          =    PARAM_MAPPING[full_name.split('.'  )[-1]]
															__a     :			Tuple          =    '''param'''

					if weight_type is not None and weight_type != "param":
										__a     :			List[Any]          =    getattr(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE  ).shape
					elif weight_type is not None and weight_type == "param":
										__a     :			List[str]          =    hf_pointer
										for attribute in hf_param_name.split('.'  ):
															__a     :			int          =    getattr(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE  )
										__a     :			int          =    shape_pointer.shape

										# let's reduce dimension
										__a     :			Dict          =    value[0]
					else:
										__a     :			Optional[int]          =    hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
										    F""" {value.shape} for {full_name}"""  )

					if weight_type == "weight":
										__a     :			str          =    value
					elif weight_type == "weight_g":
										__a     :			List[str]          =    value
					elif weight_type == "weight_v":
										__a     :			Optional[int]          =    value
					elif weight_type == "bias":
										__a     :			Dict          =    value
					elif weight_type == "param":
										for attribute in hf_param_name.split('.'  ):
															__a     :			List[str]          =    getattr(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE  )
										__a     :			Optional[Any]          =    value
					else:
										__a     :			Tuple          =    value

					logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""  )






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Optional[Any]		, _SCREAMING_SNAKE_CASE :  List[str]		, _SCREAMING_SNAKE_CASE :  Optional[int]		, _SCREAMING_SNAKE_CASE :  Tuple		, _SCREAMING_SNAKE_CASE :  Tuple  ):
					__a     :			List[str]          =    None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(_SCREAMING_SNAKE_CASE  ):
															__a     :			List[str]          =    PARAM_MAPPING[full_name.split('.'  )[-1]]
															__a     :			List[Any]          =    '''param'''

					if weight_type is not None and weight_type != "param":
										__a     :			str          =    '''.'''.join([key, weight_type]  )
					elif weight_type is not None and weight_type == "param":
										__a     :			Tuple          =    '''.'''.join([key, hf_param_name]  )
					else:
										__a     :			Union[str, Any]          =    key

					__a     :			int          =    value if '''lm_head''' in full_key else value[0]


__lowercase      :		Union[str, Any]			=      {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  List[Any]		, _SCREAMING_SNAKE_CASE :  Tuple		, _SCREAMING_SNAKE_CASE :  Dict=None		, _SCREAMING_SNAKE_CASE :  Optional[int]=None  ):
					__a     :			Union[str, Any]          =    False
					for key, mapped_key in MAPPING.items():
										__a     :			Optional[Any]          =    '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
										if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
															__a     :			Optional[int]          =    True
															if "*" in mapped_key:
																				__a     :			int          =    name.split(_SCREAMING_SNAKE_CASE  )[0].split('.'  )[-2]
																				__a     :			Union[str, Any]          =    mapped_key.replace('*'		, _SCREAMING_SNAKE_CASE  )
															if "weight_g" in name:
																				__a     :			List[Any]          =    '''weight_g'''
															elif "weight_v" in name:
																				__a     :			int          =    '''weight_v'''
															elif "bias" in name:
																				__a     :			str          =    '''bias'''
															elif "weight" in name:
																				# TODO: don't match quantizer.weight_proj
																				__a     :			Optional[Any]          =    '''weight'''
															else:
																				__a     :			Optional[int]          =    None
															if hf_dict is not None:
																				rename_dict(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE  )
															else:
																				set_recursively(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE  )
															return is_used
					return is_used






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Optional[int]		, _SCREAMING_SNAKE_CASE :  Any		, _SCREAMING_SNAKE_CASE :  int  ):
					__a     :			List[str]          =    []
					__a     :			Any          =    fairseq_model.state_dict()

					__a     :			List[str]          =    hf_model.wavaveca.feature_extractor

					for name, value in fairseq_dict.items():
										__a     :			str          =    False
										if "conv_layers" in name:
															load_conv_layer(
															    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, hf_model.config.feat_extract_norm == 'group'		, )
															__a     :			List[str]          =    True
										else:
															__a     :			Optional[Any]          =    load_wavaveca_layer(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE  )
										if not is_used:
															unused_weights.append(_SCREAMING_SNAKE_CASE  )

					logger.warning(F"""Unused weights: {unused_weights}"""  )






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Tuple		, _SCREAMING_SNAKE_CASE :  int		, _SCREAMING_SNAKE_CASE :  Tuple		, _SCREAMING_SNAKE_CASE :  int		, _SCREAMING_SNAKE_CASE :  Union[str, Any]  ):
					__a     :			str          =    full_name.split('conv_layers.'  )[-1]
					__a     :			Optional[Any]          =    name.split('.'  )
					__a     :			Tuple          =    int(items[0]  )
					__a     :			int          =    int(items[1]  )

					if type_id == 0:
										if "bias" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""  )
															__a     :			Union[str, Any]          =    value
															logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""  )
															__a     :			str          =    value
															logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""  )
															__a     :			Tuple          =    value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""  )
															__a     :			Tuple          =    value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
					else:
										unused_weights.append(_SCREAMING_SNAKE_CASE  )






@torch.no_grad()
def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Any		, _SCREAMING_SNAKE_CASE :  Union[str, Any]		, _SCREAMING_SNAKE_CASE :  Optional[Any]=None		, _SCREAMING_SNAKE_CASE :  Any=None		, _SCREAMING_SNAKE_CASE :  Any=True		, _SCREAMING_SNAKE_CASE :  str=False  ):
					if config_path is not None:
										__a     :			List[Any]          =    WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
					else:
										__a     :			Any          =    WavaVecaConfig()

					if is_seq_class:
										__a     :			List[Any]          =    read_txt_into_dict(_SCREAMING_SNAKE_CASE  )
										__a     :			Optional[Any]          =    idalabel
										__a     :			Tuple          =    WavaVecaForSequenceClassification(_SCREAMING_SNAKE_CASE  )
										__a     :			Tuple          =    WavaVecaFeatureExtractor(
										    feature_size=1		, sampling_rate=16_000		, padding_value=0		, do_normalize=_SCREAMING_SNAKE_CASE		, return_attention_mask=_SCREAMING_SNAKE_CASE		, )
										feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE  )

					elif is_finetuned:
										if dict_path:
															__a     :			Optional[Any]          =    Dictionary.load(_SCREAMING_SNAKE_CASE  )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__a     :			List[str]          =    target_dict.pad_index
															__a     :			Optional[Any]          =    target_dict.bos_index
															__a     :			Dict          =    target_dict.eos_index
															__a     :			Dict          =    len(target_dict.symbols  )
															__a     :			str          =    os.path.join(_SCREAMING_SNAKE_CASE		, 'vocab.json'  )
															if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
																				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_SCREAMING_SNAKE_CASE  )  )
																				return
															os.makedirs(_SCREAMING_SNAKE_CASE		, exist_ok=_SCREAMING_SNAKE_CASE  )
															__a     :			Optional[Any]          =    target_dict.indices

															# fairseq has the <pad> and <s> switched
															__a     :			str          =    0
															__a     :			Tuple          =    1
															with open(_SCREAMING_SNAKE_CASE		, 'w'		, encoding='utf-8'  ) as vocab_handle:
																				json.dump(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE  )
															__a     :			Any          =    WavaVecaCTCTokenizer(
															    _SCREAMING_SNAKE_CASE		, unk_token=target_dict.unk_word		, pad_token=target_dict.pad_word		, bos_token=target_dict.bos_word		, eos_token=target_dict.eos_word		, word_delimiter_token='|'		, do_lower_case=_SCREAMING_SNAKE_CASE		, )
															__a     :			Optional[Any]          =    True if config.feat_extract_norm == '''layer''' else False
															__a     :			Optional[Any]          =    WavaVecaFeatureExtractor(
															    feature_size=1		, sampling_rate=16_000		, padding_value=0		, do_normalize=_SCREAMING_SNAKE_CASE		, return_attention_mask=_SCREAMING_SNAKE_CASE		, )
															__a     :			Any          =    WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE		, tokenizer=_SCREAMING_SNAKE_CASE  )
															processor.save_pretrained(_SCREAMING_SNAKE_CASE  )

										__a     :			List[str]          =    WavaVecaForCTC(_SCREAMING_SNAKE_CASE  )
					else:
										__a     :			Optional[Any]          =    WavaVecaForPreTraining(_SCREAMING_SNAKE_CASE  )

					if is_finetuned or is_seq_class:
										__a     :			Optional[Any]          =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]		, arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
					else:
										__a     :			str          =    argparse.Namespace(task='audio_pretraining'  )
										__a     :			Dict          =    fairseq.tasks.setup_task(_SCREAMING_SNAKE_CASE  )

										__a     :			List[str]          =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		, task=_SCREAMING_SNAKE_CASE  )

					__a     :			List[Any]          =    model[0].eval()

					recursively_load_weights(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, not is_finetuned  )

					hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
	__lowercase      :		List[Any]			=      argparse.ArgumentParser()
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
	parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
	parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
	parser.add_argument(
	    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
	)
	parser.add_argument(
	    '--is_seq_class',
	    action='store_true',
	    help='Whether the model to convert is a fine-tuned sequence classification model or not',
	)
	__lowercase      :		Union[str, Any]			=      parser.parse_args()

	__lowercase      :		Dict			=      not args.not_finetuned and not args.is_seq_class
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.dict_path,
	    is_finetuned,
	    args.is_seq_class,
	)








'''simple docstring'''



lowerCAmelCase_  :							List[Any]             =    [0, 2, 4, 6, 8]
lowerCAmelCase_  :							str             =    [1, 3, 5, 7, 9]
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					list[int]       ,		A	:					int					):
							if remaining_length == 0:
														if digits[0] == 0 or digits[-1] == 0:
																					return 0

														for i in range(length // 2 - 1       ,		-1       ,		-1					):
																					remainder += digits[i] + digits[length - i - 1]

																					if remainder % 2 == 0:
																												return 0

																					remainder //= 10

														return 1

							if remaining_length == 1:
														if remainder % 2 == 0:
																					return 0

														SCREAMING_SNAKE_CASE       :   List[Any]         =      0
														for digit in range(10					):
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      digit
																					result += reversible_numbers(
																					    0       ,		(remainder + 2 * digit) // 10       ,		A       ,		A					)
														return result

							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							for digita in range(10					):
														SCREAMING_SNAKE_CASE       :   int         =      digita

														if (remainder + digita) % 2 == 0:
																					SCREAMING_SNAKE_CASE       :   str         =      ODD_DIGITS
														else:
																					SCREAMING_SNAKE_CASE       :   List[str]         =      EVEN_DIGITS

														for digita in other_parity_digits:
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      digita
																					result += reversible_numbers(
																					    remaining_length - 2       ,		(remainder + digita + digita) // 10       ,		A       ,		A       ,		)
							return result
def        UpperCAmelCase (    A	:					int = 9					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0
							for length in range(1       ,		max_power + 1					):
														result += reversible_numbers(A       ,		0       ,		[0] * length       ,		A					)
							return result


if __name__ == "__main__":
							print(f'{solution() = }')




